backend/src/main.py

@@ -100,10 +100,11 @@ def new_trip(preferences: Preferences,
     try:
         base_tour = optimizer.solve_optimization(preferences.max_time_minute, landmarks_short)
     except ArithmeticError as exc:
-        raise HTTPException(status_code=500, detail="No solution found") from exc
+        raise HTTPException(status_code=500) from exc
     except TimeoutError as exc:
         raise HTTPException(status_code=500, detail="Optimzation took too long") from exc
-
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=f"An unexpected error occurred: {str(exc)}") from exc
     t_first_stage = time.time() - start_time
     start_time = time.time()
 

backend/src/tests/test_main.py

@@ -35,8 +35,10 @@ def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
             }
         )
     result = response.json()
+    print(result)
     landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
 
+
     # Get computation time
     comp_time = time.time() - start_time
 
@@ -49,7 +51,7 @@ def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
     assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
     assert len(landmarks) > 2           # check that there is something to visit
     assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
-
+    assert 2==3
 
 def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
     """
@@ -91,7 +93,88 @@ def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
     assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
     assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
 
+'''
 
+def test_Paris(client, request) :   # pylint: disable=redefined-outer-name
+    """
+    Test n°2 : Custom test in Paris (les Halles) centre to ensure proper decision making in crowded area.
+    
+    Args:
+        client:
+        request:
+    """
+    start_time = time.time()  # Start timer
+    duration_minutes = 300
+
+    response = client.post(
+        "/trip/new",
+        json={
+            "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
+                            "nature": {"type": "nature", "score": 5},
+                            "shopping": {"type": "shopping", "score": 5},
+                            "max_time_minute": duration_minutes,
+                            "detour_tolerance_minute": 0},
+            "start": [48.86248803298562, 2.346451131285925]
+            }
+        )
+    result = response.json()
+    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
+
+    # Get computation time
+    comp_time = time.time() - start_time
+
+    # Add details to report
+    log_trip_details(request, landmarks, result['total_time'], duration_minutes)
+
+    for elem in landmarks :
+        print(elem)
+        print(elem.osm_id)
+
+    # checks :
+    assert response.status_code == 200  # check for successful planning
+    assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
+    assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
+
+
+def test_New_York(client, request) :   # pylint: disable=redefined-outer-name
+    """
+    Test n°2 : Custom test in New York (les Halles) centre to ensure proper decision making in crowded area.
+    
+    Args:
+        client:
+        request:
+    """
+    start_time = time.time()  # Start timer
+    duration_minutes = 600
+
+    response = client.post(
+        "/trip/new",
+        json={
+            "preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
+                            "nature": {"type": "nature", "score": 5},
+                            "shopping": {"type": "shopping", "score": 5},
+                            "max_time_minute": duration_minutes,
+                            "detour_tolerance_minute": 0},
+            "start": [40.72592726802, -73.9920434795]
+            }
+        )
+    result = response.json()
+    landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
+
+    # Get computation time
+    comp_time = time.time() - start_time
+
+    # Add details to report
+    log_trip_details(request, landmarks, result['total_time'], duration_minutes)
+
+    for elem in landmarks :
+        print(elem)
+        print(elem.osm_id)
+
+    # checks :
+    assert response.status_code == 200  # check for successful planning
+    assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
+    assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
 
 def test_shopping(client, request) :   # pylint: disable=redefined-outer-name
     """
@@ -128,7 +211,7 @@ def test_shopping(client, request) :   # pylint: disable=redefined-outer-name
     assert response.status_code == 200  # check for successful planning
     assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
     assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
-
+'''
 # def test_new_trip_single_prefs(client):
 #     response = client.post(
 #         "/trip/new",

backend/src/utils/cluster_manager.py

@@ -280,6 +280,6 @@ class ClusterManager:
             filtered_cluster_labels.append(np.full((label_counts[label],), label))  # Replicate the label
 
         # update the cluster points and labels with the filtered data
-        self.cluster_points = np.vstack(filtered_cluster_points)
+        self.cluster_points = np.vstack(filtered_cluster_points)        # ValueError here
         self.cluster_labels = np.concatenate(filtered_cluster_labels)
 

backend/src/utils/landmarks_manager.py

@@ -1,3 +1,4 @@
+"""Module used to import data from OSM and arrange them in categories."""
 import math, yaml, logging
 from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
 from OSMPythonTools.cachingStrategy import CachingStrategy, JSON

backend/src/utils/optimizer.py

@@ -31,6 +31,231 @@ class Optimizer:
             self.overshoot = parameters['overshoot']
 
 
+    def init_ub_time(self, landmarks: list[Landmark], max_time: int):
+        """
+        Initialize the objective function coefficients and inequality constraints.
+        -> Adds 1 row of constraints
+
+                1 row
+            + L-1 rows
+
+        -> Pre-allocates A_ub for the rest of the computations
+
+        This function computes the distances between all landmarks and stores
+        their attractiveness to maximize sightseeing. The goal is to maximize 
+        the objective function subject to the constraints A*x < b and A_eq*x = b_eq.
+
+        Args:
+            landmarks (list[Landmark]): List of landmarks.
+            max_time (int): Maximum time of visit allowed.
+
+        Returns:
+            tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality
+            constraint coefficients, and the right-hand side of the inequality constraint.
+        """
+        L = len(landmarks)
+
+        # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
+        c = np.zeros(L, dtype=np.int16)
+
+        # Coefficients of inequality constraints (left-hand side)
+        A_first = np.zeros((L, L), dtype=np.int16)
+
+        for i, spot1 in enumerate(landmarks) :
+            c[i] = -spot1.attractiveness
+            for j in range(i+1, L) :
+                if i !=j :
+                    t = get_time(spot1.location, landmarks[j].location) + spot1.duration
+                    A_first[i,j] = t
+                    A_first[j,i] = t
+
+        # Now sort and modify A_ub for each row
+        if L > 22 :
+            for i in range(L):
+                # Get indices of the 20 smallest values in row i
+                closest_indices = np.argpartition(A_first[i, :], 20)[:20]
+
+                # Create a mask for non-closest landmarks
+                mask = np.ones(L, dtype=bool)
+                mask[closest_indices] = False
+
+                # Set non-closest landmarks to 32700
+                A_first[i, mask] = 32765
+        
+        # Replicate the objective function 'c' for each decision variable (L times)
+        c = np.tile(c, L)  # This correctly expands 'c' to L*L
+
+        return c, A_first.flatten(), [max_time*self.overshoot]
+
+
+    def respect_number(self, L, max_landmarks: int):
+        """
+        Generate constraints to ensure each landmark is visited only once and cap the total number of visited landmarks.
+        -> Adds L-1 rows of constraints
+
+        Args:
+            L (int): Number of landmarks.
+
+        Returns:
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+        """
+        # First constraint: each landmark is visited exactly once
+        A = np.zeros((L-1, L*L), dtype=np.int8)
+        b = []
+        for i in range(1, L-1):
+            A[i-1, L*i:L*(i+1)] = np.ones(L, dtype=np.int8)
+            b.append(1)
+
+        # Second constraint: cap the total number of visits
+        A[-1, :] = np.ones(L*L, dtype=np.int8)
+        b.append(max_landmarks+2)
+        return A, b
+
+
+    def break_sym(self, L):
+        """
+        Generate constraints to prevent simultaneous travel between two landmarks
+        in both directions. Constraint to not have d14 and d41 simultaneously. 
+        Does not prevent cyclic paths with more elements
+        -> Adds a variable number of rows of constraints
+
+        Args:
+            L (int): Number of landmarks.
+
+        Returns:
+            tuple[np.ndarray, list[int]]:   Inequality constraint coefficients and 
+                                            the right-hand side of the inequality constraints.
+        """
+        b = []
+        upper_ind = np.triu_indices(L,0,L)
+        up_ind_x = upper_ind[0]
+        up_ind_y = upper_ind[1]
+
+        A = np.zeros((len(up_ind_x[1:]),L*L), dtype=np.int8)
+        for i, _ in enumerate(up_ind_x[1:]) :
+            if up_ind_x[i] != up_ind_y[i] :
+                A[i, up_ind_x[i]*L + up_ind_y[i]] = 1
+                A[i, up_ind_y[i]*L + up_ind_x[i]] = 1
+                b.append(1)
+
+        return A[~np.all(A == 0, axis=1)], b
+
+
+    def init_eq_not_stay(self, L: int): 
+        """
+        Generate constraints to prevent staying in the same position (e.g., removing d11, d22, d33, etc.).
+        -> Adds 1 row of constraints
+
+        Args:
+            L (int): Number of landmarks.
+
+        Returns:
+            tuple[list[np.ndarray], list[int]]: Equality constraint coefficients and the right-hand side of the equality constraints.
+        """
+        l = np.zeros((L, L), dtype=np.int8)
+        
+        # Set diagonal elements to 1 (to prevent staying in the same position)
+        np.fill_diagonal(l, 1)
+
+        return l.flatten(), [0]
+
+
+    def respect_user_must_do(self, landmarks: list[Landmark]) :
+        """
+        Generate constraints to ensure that landmarks marked as 'must_do' are included in the optimization.
+        -> Adds a variable number of rows of constraints BUT CAN BE PRE COMPUTED
+
+
+        Args:
+            landmarks (list[Landmark]): List of landmarks, where some are marked as 'must_do'.
+
+        Returns:
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+        """
+        L = len(landmarks)
+        A = np.zeros((L, L*L), dtype=np.int8)
+        b = []
+
+        for i, elem in enumerate(landmarks) :
+            if elem.must_do is True and elem.name not in ['finish', 'start']:
+                A[i, i*L:i*L+L] = np.ones(L, dtype=np.int8)
+                b.append(1)
+
+        return A[~np.all(A == 0, axis=1)], b
+    
+
+    def respect_user_must_avoid(self, landmarks: list[Landmark]) :
+        """
+        Generate constraints to ensure that landmarks marked as 'must_avoid' are skipped
+        in the optimization.
+        -> Adds a variable number of rows of constraints BUT CAN BE PRE COMPUTED
+
+        Args:
+            landmarks (list[Landmark]): List of landmarks, where some are marked as 'must_avoid'.
+
+        Returns:
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+        """
+        L = len(landmarks)
+        A = np.zeros((L, L*L), dtype=np.int8)
+        b = []
+
+        for i, elem in enumerate(landmarks) :
+            if elem.must_do is True and i not in [0, L-1]:
+                A[i, i*L:i*L+L] = np.ones(L, dtype=np.int8)
+                b.append(0)
+
+        return A[~np.all(A == 0, axis=1)], b
+
+
+    # Constraint to ensure start at start and finish at goal
+    def respect_start_finish(self, L: int):
+        """
+        Generate constraints to ensure that the optimization starts at the designated
+        start landmark and finishes at the goal landmark.
+        -> Adds 3 rows of constraints
+        Args:
+            L (int): Number of landmarks.
+
+        Returns:
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+        """
+
+        A = np.zeros((3, L*L), dtype=np.int8)
+
+        A[0, :L] = np.ones(L, dtype=np.int8)        # sets departures only for start (horizontal ones)
+        for k in range(L-1) :
+            A[2, k*L] = 1
+            if k != 0 :
+                A[1, k*L+L-1] = 1                   # sets arrivals only for finish (vertical ones)
+        A[2, L*(L-1):] = np.ones(L, dtype=np.int8)  # prevents arrivals at start and departures from goal
+        b = [1, 1, 0]
+
+        return A, b
+
+
+    def respect_order(self, L: int): 
+        """
+        Generate constraints to tie the optimization problem together and prevent 
+        stacked ones, although this does not fully prevent circles.
+        -> Adds L-2 rows of constraints
+
+        Args:
+            L (int): Number of landmarks.
+
+        Returns:
+            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+        """
+
+        A = np.zeros((L-2, L*L), dtype=np.int8)
+        b = [0]*(L-2)
+        for i in range(1, L-1) :           # Prevent stacked ones
+            for j in range(L) :
+                A[i-1, i + j*L] = -1
+            A[i-1, i*L:(i+1)*L] = np.ones(L, dtype=np.int8)
+
+        return A, b
+
 
     # Prevent the use of a particular solution
     def prevent_config(self, resx):
@@ -164,236 +389,6 @@ class Optimizer:
         return order, all_journeys_nodes
 
 
-
-    def init_ub_time(self, landmarks: list[Landmark], max_time: int):
-        """
-        Initialize the objective function coefficients and inequality constraints.
-
-        This function computes the distances between all landmarks and stores
-        their attractiveness to maximize sightseeing. The goal is to maximize 
-        the objective function subject to the constraints A*x < b and A_eq*x = b_eq.
-
-        Args:
-            landmarks (list[Landmark]): List of landmarks.
-            max_time (int): Maximum time of visit allowed.
-
-        Returns:
-            tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality
-            constraint coefficients, and the right-hand side of the inequality constraint.
-        """
-        
-        # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
-        c = []
-        # Coefficients of inequality constraints (left-hand side)
-        A_ub = []
-
-        for spot1 in landmarks :
-            dist_table = [0]*len(landmarks)
-            c.append(-spot1.attractiveness)
-            for j, spot2 in enumerate(landmarks) :
-                t = get_time(spot1.location, spot2.location) + spot1.duration
-                dist_table[j] = t
-            closest = sorted(dist_table)[:15]
-            for i, dist in enumerate(dist_table) :
-                if dist not in closest :
-                    dist_table[i] = 32700
-            A_ub += dist_table
-        c = c*len(landmarks)
-
-        return c, A_ub, [max_time*self.overshoot]
-
-
-    def respect_number(self, L, max_landmarks: int):
-        """
-        Generate constraints to ensure each landmark is visited only once and cap the total number of visited landmarks.
-
-        Args:
-            L (int): Number of landmarks.
-
-        Returns:
-            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
-        """
-
-        ones = [1]*L
-        zeros = [0]*L
-        A = ones + zeros*(L-1)
-        b = [1]
-        for i in range(L-1) :
-            h_new = zeros*i + ones + zeros*(L-1-i)
-            A = np.vstack((A, h_new))
-            b.append(1)
-
-        A = np.vstack((A, ones*L))
-        b.append(max_landmarks+1)
-
-        return A, b
-
-
-    # Constraint to not have d14 and d41 simultaneously. Does not prevent cyclic paths with more elements
-    def break_sym(self, L):
-        """
-        Generate constraints to prevent simultaneous travel between two landmarks in both directions.
-
-        Args:
-            L (int): Number of landmarks.
-
-        Returns:
-            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
-        """
-
-        upper_ind = np.triu_indices(L,0,L)
-
-        up_ind_x = upper_ind[0]
-        up_ind_y = upper_ind[1]
-
-        A = [0]*L*L
-        b = [1]
-
-        for i, _ in enumerate(up_ind_x[1:]) :
-            l = [0]*L*L
-            if up_ind_x[i] != up_ind_y[i] :
-                l[up_ind_x[i]*L + up_ind_y[i]] = 1
-                l[up_ind_y[i]*L + up_ind_x[i]] = 1
-
-                A = np.vstack((A,l))
-                b.append(1)
-
-        return A, b
-
-
-    def init_eq_not_stay(self, L: int): 
-        """
-        Generate constraints to prevent staying in the same position (e.g., removing d11, d22, d33, etc.).
-
-        Args:
-            L (int): Number of landmarks.
-
-        Returns:
-            tuple[list[np.ndarray], list[int]]: Equality constraint coefficients and the right-hand side of the equality constraints.
-        """
-
-        l = [0]*L*L
-
-        for i in range(L) :
-            for j in range(L) :
-                if j == i :
-                    l[j + i*L] = 1
-        
-        l = np.array(np.array(l), dtype=np.int8)
-
-        return [l], [0]
-
-
-    def respect_user_must_do(self, landmarks: list[Landmark]) :
-        """
-        Generate constraints to ensure that landmarks marked as 'must_do' are included in the optimization.
-
-        Args:
-            landmarks (list[Landmark]): List of landmarks, where some are marked as 'must_do'.
-
-        Returns:
-            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
-        """
-
-        L = len(landmarks)
-        A = [0]*L*L
-        b = [0]
-
-        for i, elem in enumerate(landmarks[1:]) :
-            if elem.must_do is True and elem.name not in ['finish', 'start']:
-                l = [0]*L*L
-                l[i*L:i*L+L] = [1]*L        # set mandatory departures from landmarks tagged as 'must_do'
-
-                A = np.vstack((A,l))
-                b.append(1)
-
-        return A, b
-    
-
-    def respect_user_must_avoid(self, landmarks: list[Landmark]) :
-        """
-        Generate constraints to ensure that landmarks marked as 'must_avoid' are skipped in the optimization.
-
-        Args:
-            landmarks (list[Landmark]): List of landmarks, where some are marked as 'must_avoid'.
-
-        Returns:
-            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
-        """
-
-        L = len(landmarks)
-        A = [0]*L*L
-        b = [0]
-
-        for i, elem in enumerate(landmarks[1:]) :
-            if elem.must_avoid is True and elem.name not in ['finish', 'start']:
-                l = [0]*L*L
-                l[i*L:i*L+L] = [1]*L        
-
-                A = np.vstack((A,l))
-                b.append(0)             # prevent departures from landmarks tagged as 'must_do'
-
-        return A, b
-
-
-    # Constraint to ensure start at start and finish at goal
-    def respect_start_finish(self, L: int):
-        """
-        Generate constraints to ensure that the optimization starts at the designated start landmark and finishes at the goal landmark.
-
-        Args:
-            L (int): Number of landmarks.
-
-        Returns:
-            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
-        """
-
-        l_start = [1]*L + [0]*L*(L-1)   # sets departures only for start (horizontal ones)
-        l_start[L-1] = 0                # prevents the jump from start to finish
-        l_goal = [0]*L*L                # sets arrivals only for finish (vertical ones)
-        l_L = [0]*L*(L-1) + [1]*L       # prevents arrivals at start and departures from goal
-        for k in range(L-1) :           # sets only vertical ones for goal (go to)
-            l_L[k*L] = 1
-            if k != 0 :
-                l_goal[k*L+L-1] = 1     
-
-        A = np.vstack((l_start, l_goal))
-        b = [1, 1]
-        A = np.vstack((A,l_L))
-        b.append(0)
-
-        return A, b
-
-
-    def respect_order(self, L: int): 
-        """
-        Generate constraints to tie the optimization problem together and prevent stacked ones, although this does not fully prevent circles.
-
-        Args:
-            L (int): Number of landmarks.
-
-        Returns:
-            tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
-        """
-
-        A = [0]*L*L
-        b = [0]
-        for i in range(L-1) :           # Prevent stacked ones
-            if i == 0 or i == L-1:      # Don't touch start or finish
-                continue
-            else : 
-                l = [0]*L
-                l[i] = -1
-                l = l*L
-                for j in range(L) :
-                    l[i*L + j] = 1
-
-                A = np.vstack((A,l))
-                b.append(0)
-
-        return A, b
-
-
     def link_list(self, order: list[int], landmarks: list[Landmark])->list[Landmark] :
         """
         Compute the time to reach from each landmark to the next and create a list of landmarks with updated travel times.
@@ -455,20 +450,24 @@ class Optimizer:
 
         # SET CONSTRAINTS FOR INEQUALITY
         c, A_ub, b_ub = self.init_ub_time(landmarks, max_time)          # Add the distances from each landmark to the other
+        
         A, b = self.respect_number(L, max_landmarks)                    # Respect max number of visits (no more possible stops than landmarks). 
-        A_ub = np.vstack((A_ub, A), dtype=np.int16)
+        A_ub = np.vstack((A_ub, A))
         b_ub += b
+
         A, b = self.break_sym(L)                                         # break the 'zig-zag' symmetry
-        A_ub = np.vstack((A_ub, A), dtype=np.int16)
+        A_ub = np.vstack((A_ub, A))
         b_ub += b
 
 
         # SET CONSTRAINTS FOR EQUALITY
         A_eq, b_eq = self.init_eq_not_stay(L)                            # Force solution not to stay in same place
         A, b = self.respect_user_must_do(landmarks)                      # Check if there are user_defined must_see. Also takes care of start/goal
+        if len(b) > 0 :
             A_eq = np.vstack((A_eq, A), dtype=np.int8)
             b_eq += b
         A, b = self.respect_user_must_avoid(landmarks)                      # Check if there are user_defined must_see. Also takes care of start/goal
+        if len(b) > 0 :
             A_eq = np.vstack((A_eq, A), dtype=np.int8)
             b_eq += b
         A, b = self.respect_start_finish(L)                  # Force start and finish positions
@@ -477,6 +476,7 @@ class Optimizer:
         A, b = self.respect_order(L)                         # Respect order of visit (only works when max_time is limiting factor)
         A_eq = np.vstack((A_eq, A), dtype=np.int8)
         b_eq += b
+        # until here opti
         
         # SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
         x_bounds = [(0, 1)]*L*L
@@ -484,7 +484,7 @@ class Optimizer:
         # Solve linear programming problem
         res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
 
-        # Raise error if no solution is found
+        # Raise error if no solution is found. FIXME: for now this throws the internal server error
         if not res.success :
             raise ArithmeticError("No solution could be found, the problem is overconstrained. Try with a longer trip (>30 minutes).")
 
@@ -505,7 +505,6 @@ class Optimizer:
             res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
             if not res.success :
                 raise ArithmeticError("Solving failed because of overconstrained problem")
-                return None
             order, circles = self.is_connected(res.x)
             #nodes, edges = is_connected(res.x)
             if circles is None :

backend/src/utils/refiner.py

@@ -1,7 +1,9 @@
-import yaml, logging
-
-from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
+"""Allows to refine the tour by adding more landmarks and making the path easier to follow."""
+import logging
 from math import pi
+import yaml
+from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
+
 
 from ..structs.landmark import Landmark
 from . import take_most_important, get_time_separation